Project - Featurisation & Model Tuning

by HARI SAMYNAATH S

User Defined functions / classes and library initiations

Project

DOMAIN: Semiconductor manufacturing process
• CONTEXT:
A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs. These signals can be used as features to predict the yield type. And by analysing and trying out different combinations of features, essential signals that are impacting the yield type can be identified.
• DATA DESCRIPTION: sensor-data.csv : (1567, 592)
The data consists of 1567 datapoints each with 591 features. The dataset presented in this case represents a selection of such features where each example represents a single production entity with associated measured features and the labels represent a simple pass/fail yield for in house line testing. Target column “ –1” corresponds to a pass and “1” corresponds to a fail and the data time stamp is for that speci ic test point.
• PROJECT OBJECTIVE:
We will build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not.

Steps and tasks:
1.Import and understand the data.
A. Import ‘signal-data.csv’ as DataFrame.
B. Print 5 point summary and share at least 2 observations.

every column is a numeric data except for Time column
later, lets see if we could extract features from Time column else drop it
Also the Time column seems to have duplicates, which could be the same with all the other columns too.
need to confirm to drop those.

There are few constant columns like "13","42",...
There are few extreme skewed or quasi-constant columns like "4","21"...
There are few near-perfect bell curves like "24"

Need to review and remove columns that doesn't add information to target In reference to the target, the dataset seems imbalanced as more than 75% of data corresponds to -1

2.Data cleansing:
A. Write a for loop which will remove all the features with 20%+ Null values and impute rest with mean of the feature.
B. Identify and drop the features which are having same value for all the rows.

safe to continue without dropping any records

none found, hence lets proceed

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Let us set a base line model using DecisionTreeClassifier

pretty impressive accuracy and low execution time
but unfortunately the precision, recall and f1_score for FAIL class (+1) is very poor
they are poor in training data prediction, probably because of imbalanced data
in the test data prediction, those have fallen even lower, indicating over-fit model
Lets build on our modelling

before proceeding further, lets extract some timestamp features, few polynomial features & inherent clusters

2.Data cleansing:
C. Drop other features if required using relevant functional knowledge. Clearly justify the same.

the variances (or standard deviations) of several fearutes are condensed below unity
this indicates a that several features would not contribute to the model learning
though z-score tranformation will shift & rescale the distributions, it would also leverage all the noises in the data towards model learning
hence let us use few feature selection techniques to shrink our dataset

the f1 score and recall for FAIL class has improved
lets study further

using custom method based on published paper from
SCFS (Standard deviation and Cosine similarity based Feature Selection)
Reference article for feature scoring
https://www.frontiersin.org/articles/10.3389/fgene.2021.684100/full
Credits to: Juanying Xie, Mingzhao Wang, Shengquan Xu, Zhao Huang and Philip W. Grant

Explanation & Justification to use the method
The discernibility of a feature, refers to its distinguishable capability between categories
Feature selection aims to detect the features whose distinguishable capability is strong while the redundancy between them is less
To represent the redundancy between a feature and the other features, cosine similarity is used
Feature independence is deduced from cosine similarity ( in 3 possible ways)
The method guarantees that a feature will have the maximal independence as far as possible once it has the maximal discernibility

the least standard deviation is above 5 units, which is remarkable improvement from original dataset
lets check our model learnability

the model has improved further, showcasing better recall & f1 scores in FAIL class
lets try to perform SCFS after standardisation

this has improved the model learning better than the previous model with better f1 for FAIL class
this further justifies the use of the SCFS method
hence SCFS will be used after standardisation of the dataset
lets try tuning the threshold value

based on the above results, lets choose the threshold of 50% for we find best f1 score on test data.

tuning has not improved the FAIL class scores
lets move to futher optimising the features

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by now, the following project statement have been covered and mentioned here to keep track

  1. Import and understand the data.
    A. Import ‘signal-data.csv’ as DataFrame.
    B. Print 5 point summary and share at least 2 observations.
  2. Data cleansing:
    A. Write a for loop which will remove all the features with 20%+ Null values and impute rest with mean of the feature.
    B. Identify and drop the features which are having same value for all the rows.
    C. Drop other features if required using relevant functional knowledge. Clearly justify the same.
  3. Data pre-processing:
    A. Segregate predictors vs target attributes.
    B. Check for target balancing and fix it if found imbalanced.
    C. Perform train-test split and standardise the data or vice versa if required.
  4. Model training, testing and tuning:
    A. Use any Supervised Learning technique to train a model.

2.Data cleansing:
D. Check for multi-collinearity in the data and take necessary action.

there are 485 cases of high correlation, indicating high multi-collinearity among the dataset
lets check on SCFS trimmed data

interestingly there is only 4 cases of high correlation
lets investigate further, using Variance Inflation Factors
by definition, the variance inflation factor is a measure for the increase of the variance of the parameter estimates if an additional variable, given by exog_idx is added to the linear regression. It is a measure for multicollinearity of the design matrix, exog.
One recommendation is that if VIF is greater than 5, then the explanatory variable given by exog_idx is highly collinear with the other explanatory variables, and the parameter estimates will have large standard errors because of this.
hence features having VIF above 5 needs to be studied for dropping

considering the two VIF distributions, it could be meaningful in starting with the SCFS dataset
As it has already taken cosine similarities into picture, most multi-collienar features would have been removed

succesfully features of high VIF have been dropped
leaving behind 52 features

as expected, there are no cases of multi-collinearity, shown by no correlation above 0.75

the FAIL class scores has improved slighty in terms of recall

still the poor variance features are still available. lets try the 75% SCFS data

this leaves us with only 40 features

this reduces our model performance, hence we shall stick with 60% timmed SCFS data optimised with VIF (DTC_4)

2.Data cleansing:
E. Make all relevant modifications on the data using both functional/logical reasoning/assumptions.

skew correction has helped in terms of FAIL class scores and accuracy.

apart from above skew correction,
timestamp feature extraction
polynomial feature axtraction and
were performed earlier in the notebook